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| import re | |
| # import pickle | |
| class Classifier: | |
| def __init__(self, dict_reemplazo, ngram_vectorizer, transformer, svm_model) -> None: | |
| self.dict_reemplazo = dict_reemplazo | |
| self.ngram_vectorizer = ngram_vectorizer | |
| self.transformer = transformer | |
| self.svm_model = svm_model | |
| def reemplazar_caracteres_diferentes(self, texto, dictionary): | |
| return texto.translate(dictionary) | |
| def eliminar_ruido(self, texto, caracteres): | |
| nuevo_texto = texto | |
| for c in caracteres: | |
| nuevo_texto = re.sub(c, '', nuevo_texto) | |
| return nuevo_texto | |
| def eliminar_espacios(self, string): | |
| nuevo_string = string.strip() | |
| nuevo_string = ' '.join(nuevo_string.split()) | |
| return nuevo_string | |
| def predict(self, npt_txt): | |
| txt = self.eliminar_espacios( | |
| self.eliminar_ruido( | |
| self.reemplazar_caracteres_diferentes( | |
| self.eliminar_espacios( | |
| self.eliminar_ruido(npt_txt, [r'[^\w\s^\´\’]'])), self.dict_reemplazo), [r'\d+', '_'])) | |
| vctr = self.transformer.transform(self.ngram_vectorizer.transform([txt])) | |
| return 'Español' if self.svm_model.predict(vctr)[0] == 0 else 'Quechua' | |
| # if __name__ == '__main__': | |
| # with open('dict_reemplazo', 'rb') as f: | |
| # dict_reemplazo = pickle.load(f) | |
| # with open('ngram_vectorizer', 'rb') as f: | |
| # ngram_vectorizer = pickle.load(f) | |
| # with open('transformer', 'rb') as f: | |
| # transformer = pickle.load(f) | |
| # with open('svm_model', 'rb') as f: | |
| # svm_model = pickle.load(f) | |
| # classifier = Classifier(dict_reemplazo, ngram_vectorizer, transformer, svm_model) | |
| # with open('classifier.pickle', 'wb') as f: | |
| # pickle.dump(classifier, f) | |
| # with open('classifier.pickle', 'rb') as f: | |
| # my_classifier = pickle.load(f) | |
| # for txt in ['¿Maytaq ashkallanchikega', 'Entonces el Inka dijo ¡Mach\'a!', '¡Aragan kanki wamraqa', 'Señora, ¿yanapariwayta atiwaqchu?', '¿A dónde vas?', '324#@$%']: | |
| # print (my_classifier.predict(txt)) | |